Remote Sensing of the Hydraulic Environment in Gravel‐Bed Rivers

Marcus, W. Andrew

doi:10.1002/9781119952497.ch21

Cited by 25 publications

(34 citation statements)

References 89 publications

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“…Topographic mapping using airborne LiDAR technology presents the opportunity for high-resolution remote sensing of channel process and form (e.g., Snyder, 2009;Hohenthal et al, 2011;Marcus, 2012). Airborne LiDAR surveys yield DEMs with 1 m pixels, and these are now commonly available throughout the United States and elsewhere in the world.…”

Section: Introductionmentioning

confidence: 99%

Predicting grain size in gravel-bedded rivers using digital elevation models: Application to three Maine watersheds

Snyder

Nesheim

Wilkins

et al. 2012

Geological Society of America Bulletin

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Riverbed grain size controls suitability of spawning habitat for threatened fi sh species. Motivated by this relationship, we developed a model that uses digital elevation models (DEMs) to predict bed grain size. We tested the accuracy of our model and two existing models with channel measurements from high-resolution airborne light detection and ranging (LiDAR) DEMs. All three models assume that bed grain size is a function of reach-average high-fl ow channel hydraulics (measured by shear stress or stream power). Our test data are fi eld measurements of median grain size (D 50 ) at 276 stations along four rivers in Maine. Pleistocene continental glaciation strongly infl uences the longitudinal profi les, which have alternating steep and gradual segments. We exploit the resulting variations in sediment supply to understand the controls on model success or failure in predicting bed grain size. Results show that all three models have ~70% success in predicting D 50 within a factor of two overall, and better where the rivers are coarse gravel bedded (~80% success where D 50 ≥ 16 mm). This similarity is unsurprising given that the models primarily rely on channel gradient (S) and drainage area as inputs. Measurements of S from LiDAR DEMs yield only a modest improvement in model success over those from topographic maps. We fi nd that our model works best in sediment-starved steep reaches. Model failures fall into two broad cate gories:(1) relatively fine-grained (D 50 < 16 mm) depo sitional reaches where our assumption of a constant, bankfull threshold for bed mobilization may be invalid; and (2) reaches where local variations in hydraulic roughness and/or sediment supply control D 50 . We argue that models based on airborne infrared LiDAR DEMs may reach a maximum around 80%-85% accuracy due to these sub-reach-scale factors, which cannot be easily measured from DEMs. The overall success of the models in predicting grain size indicates that the morphology of these channels has adjusted to the imposed S and sediment load during the ~15 k.y. since deglaciation and through the period of anthropogenic channel change over the past three centuries.

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Section: Introductionmentioning

confidence: 99%

Predicting grain size in gravel-bedded rivers using digital elevation models: Application to three Maine watersheds

Snyder

Nesheim

Wilkins

et al. 2012

Geological Society of America Bulletin

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“…Clearly, although it is theoretically possible to measure bankfull width manually, it becomes impractical if hundreds or thousands of river width values must be obtained. During the past two decades, advances in remote-sensing technology have allowed the fast, precise and effective acquisition of topographic information with high quality (see Tarolli, 2014 for a full review), and the field of fluvial geomorphology has seen increased application of high resolution surveying technologies to characterise river bathymetry and floodplain topography (Heritage et al, 2009;Milan et al, 2011;Marcus, 2012;Sofia et al, 2014aSofia et al, , 2014b. Different data-driven methods have been proposed for channel geometry (Pavelsky and Smith, 2008;McKean et al, 2009;Johansen et al, 2011;Biron et al, 2013;Fisher et al, 2013;Güneralp et al, 2014;Bangen et al, 2014); however, an automated method for continuously extracting reach-scale width values from raster-based imagery would provide valuable insight into many hydrologic studies (Pavelsky and Smith, 2008).…”

Section: Background and Aims Of Researchmentioning

confidence: 99%

Downstream hydraulic geometry relationships: Gathering reference reach-scale width values from LiDAR

et al. 2015

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“…An extensive overview about active and passive remote sensing techniques in river environments is given by Marcus (2010), in which the main focus is on the water course. From a LiDAR perspective three main topics are relevant for hydraulic studies: (i) the extent of the water surface, (ii) the digital elevation or terrain data of the river bed and the inundation area and (iii) the riparian vegetation, artificial objects (e.g.…”

Section: Lidar Used In Hydraulics and Hydrologymentioning

confidence: 99%

Vertical Vegetation Structure Analysis and Hydraulic Roughness Determination Using Dense Als Point Cloud Data - A Voxel Based Approach

Vetter

Höfle

Hollaus

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this contribution the complexity of the vertical vegetation structure, based on dense airborne laser scanning (ALS) point cloud data (25 echoes/m 2 ), is analyzed to calculate vegetation roughness for hydraulic applications. Using the original 3D ALS point cloud, three levels of abstractions are derived (cells, voxels and connections) to analyze ALS data based on a 1x1 m 2 raster over the whole data set. A voxel structure is used to count the echoes in predefined detrended height levels within each cell. In general, it is assumed that the number of voxels containing echoes is an indicator for elevated objects and consequently for increased roughness. Neighboring voxels containing at least one data point are merged together to connections. An additional height threshold is applied to connect vertical neighboring voxels with a certain distance in between. Thus, the connections indicate continuous vegetation structures. The height of the surface near or lowest connection is an indicator for hydrodynamic roughness coefficients. For cells, voxels and connections the laser echoes are counted within the structure and various statistical measures are calculated. Based on these derived statistical parameters a rule-based classification is developed by applying a decision tree to assess vegetation types. Roughness coefficient values such as Manning's n are estimated, which are used as input for 2D hydrodynamic-numerical modeling. The estimated Manning's values from the ALS point cloud are compared with a traditional Manning's map. Finally, the effect of these two different Manning's n maps as input on the 2D hydraulics are quantified by calculating a height difference model of the inundated depth maps. The results show the large potential of using the entire vertical vegetation structure for hydraulic roughness estimation.

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Remote Sensing of the Hydraulic Environment in Gravel‐Bed Rivers

Cited by 25 publications

References 89 publications

Predicting grain size in gravel-bedded rivers using digital elevation models: Application to three Maine watersheds

Predicting grain size in gravel-bedded rivers using digital elevation models: Application to three Maine watersheds

Downstream hydraulic geometry relationships: Gathering reference reach-scale width values from LiDAR

Vertical Vegetation Structure Analysis and Hydraulic Roughness Determination Using Dense Als Point Cloud Data - A Voxel Based Approach

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